Robust Fault Detection and Isolation Using Robust l{sub}1 Estimation

摘要

This paper considers the application of robust l{sub}1 estimation to fault robust fault detection and isolation. This is accomplished by developing a series, or bank, of robust estimators (full-order observers), each of which is designed such that the residual will be sensitive to a certain fault (or faults) while insensitive to the remaining faults. Robustness is incorporated by assuring that the residual remains insensitive to exogenous disturbances as well as modeling uncertainty. Mixed structured singular value and l{sub}1 theories are used to develop the appropriate threshold logic to evaluate the outputs of the estimators used for determining the occurrence and location of a fault. A real-coded genetic algorithm is used to obtain the optimal estimator gain matrices. This approach to FDI is successfully demonstrated using a linearized model of a jet engine.